FaceForensics++数据库下载（小白友好&超详细版教程）
FaceForensics++数据库下载（超详细版教程）相信很多做deepfake相关研究的朋侪，在对模型举行测试或者对潜前人的研究举行复现时，都必要下载一系列数据库并举行预处置惩罚等操纵，而FaceForensics++数据库是一个由数千个利用不同DeepFake方法操纵的视频组成，并包含四个假子数据集，即DeepFake Detection (DFD)， DeepFake (DF)， Face2Face (F2F)和FaceSwap (FS)。
这里提供一个本人切身利用过并下载完成后的方法（会提供下载脚本，笔者本身也是小白所以说的会非常详细而且简朴易懂）。
找到dataset的README文件
进入github上搜索到FaceForensics++数据库的README文件，网址如下：
[数据库GitHub上有关数据库部门的阐明文件网址]: FaceForensics/dataset/README.md at master · ondyari/FaceForensics (github.com)(https://github.com/ondyari/FaceForensics/blob/master/dataset/README.md)
README文件对数据库的下载和组成部门，包括各个数据库所必要占用空间的大小做了详细的阐明（其中的C23和C40代表压缩参数）：
https://i-blog.csdnimg.cn/blog_migrate/8f93a91786b017461620c8333860d8e6.png#pic_center
注意：接下来的操纵都必要在挂署理的前提下实现
获取下载脚本并并保存到本地
下载脚本的获取，必要填写一个README文件上面提供的表单，链接如下：
当然，你也可以通过README文件的以下语句中的蓝字进入该链接
https://i-blog.csdnimg.cn/blog_migrate/a076ee08d4aa14734e24fa7608eb0a1b.png#pic_center
表单上注意填写好自己的邮箱（qq邮箱是可以的），其他的可以随便填一下，大概就是用英文稍微介绍一下自己的项目，笔者这里随便填的，没有任何问题。
发送表单以后，会收到一封来自数据集研发者的邮件，上面会提供他们研发的一系列的数据集，包括FaceForensics++的下载脚本链接和Github链接，如下：
https://i-blog.csdnimg.cn/blog_migrate/51d6adddbe31e7e4532d6ecb2aa2e6f7.png#pic_center
点击第一个“here”进入脚本页，我这里是直接把脚本复制，然后在电脑桌面建立一个txt文件并把脚本粘进去，接着把文件后缀名称改为“.py”即可。
CMD窗口下载数据库
打开cmd窗口
确保你的文件定名为FaceForensics++.py并保存在你的桌面（当然也可以是别的名字，也可以保存到别的地方，但是为了后面下令可以简朴一点笔者以为最好放在桌面）
Cmd窗口输入下令：
cd Desktop
接着cmd窗口会表现已经进入桌面，接着输入下载下令：
下面是对github上对于下载下令的表明（包括的各部门的寄义）：
python FaceForensics++.py
//前面的python指你的电脑本身的python.exe文件，注意并不一定是“python”,需要观察你自己下载的python中的pythin.exe文件的命名是什么，比如笔者下载的python运行文件的命名是python3.11.exe，所以这里就应该python3.11
<output path>
//这里意思是数据下载的地址，即你的数据集要放在哪里（注意存储空间要足够）
-d <dataset type, e.g., Face2Face, original or all>
//如果你要下载FaceForensics++全部直接-all即可，也可以选择FaceForensics++数据集其中的一项来下载
-c <compression quality, e.g., c23 or raw>
//这里指压缩参数选择，如果想要下载原始数据可以选-raw,笔者下载的是-23压缩版
-t <file type, e.g., videos, masks or models>
//文件下载的类型-video即可下载deepfake的video
注意，第一个下令可以尝试在你的cmd窗口直接输入python看看是否是你的python运行文件名称，好比当笔者在cmd窗口输入"python3.11"时，会输出：
https://i-blog.csdnimg.cn/blog_migrate/d2d812ef913372d42ce44c070a0f2519.png#pic_center
就阐明我的第一个下令应该是"python3.11"，也就是利用python编译运行我们的脚本文件
好比，要在D盘上的FaceForensics++文件里下载FaceForensics++数据集全部视频，以C23参数压缩，下令可以是：
python3.11 FaceForensics.py E:/FaceForensics++ -d all -c c23 -t videos
注意，运行过程中如果出现“502 BadGateway”提示，可能是你的服务不能利用脚本默认的，而是必要更改，脚本里面提供了三个server可供选择，分别是EU,EU2和CA，对应了欧洲1，2和加拿大，默认利用的是EU，脚本这部门代码如下：
parser.add_argument('--server', type=str, default='EU',
help='Server to download the data from. If you '
'encounter a slow download speed, consider '
'changing the server.',
choices=SERVERS
)
args = parser.parse_args()
# URLs
server = args.server
if server == 'EU':
server_url = 'http://canis.vc.in.tum.de:8100/'
elif server == 'EU2':
server_url = 'http://kaldir.vc.in.tum.de/faceforensics/'
elif server == 'CA':
server_url = 'http://falas.cmpt.sfu.ca:8100/'
else:
raise Exception('Wrong server name. Choices: {}'.format(str(SERVERS)))
args.tos_url = server_url + 'webpage/FaceForensics_TOS.pdf'
args.base_url = server_url + 'v3/'
args.deepfakes_model_url = server_url + 'v3/manipulated_sequences/' + \
'Deepfakes/models/'
return args
所以，在你的CMD窗口可以指定以下server，笔者在出错以后，将server改为EU2就可以顺遂下载，总体下令如下：
cd Desktop
#回车python3.11 FaceForensics.py E:/FaceForensics++ --server EU2 -d all -c c23 -t videos PS:–server EU2即指定server为EU2
接下来数据就会开始下载（会有进度条表现下载情况），如下图：
https://i-blog.csdnimg.cn/blog_migrate/bfd2eca91da49b2b0f00be8358cd04a0.png#pic_center
由于挂署理，所以可能会出现不稳固的克制下载的情况，如果碰到程序停止，没有关系，确保网络、署理精确连接后重新输入下载下令即可，它会主动跳过已经下载好的文件，继续下载其他文件。
建议下载数据集时只管包管网速较快，而且署理稳固（否则一停止就重新输入一遍下令很麻烦）。
脚本
附：邮件提供的FaceForensics++脚本如下：
#!/usr/bin/env python""" Downloads FaceForensics++ and Deep Fake Detection public data releaseExample usage: see -h or https://github.com/ondyari/FaceForensics"""# -*- coding: utf-8 -*-import argparseimport osimport urllibimport urllib.requestimport tempfileimport timeimport sysimport jsonimport randomfrom tqdm import tqdmfrom os.path import join# URLs and filenamesFILELIST_URL = 'misc/filelist.json'DEEPFEAKES_DETECTION_URL = 'misc/deepfake_detection_filenames.json'DEEPFAKES_MODEL_NAMES = ['decoder_A.h5', 'decoder_B.h5', 'encoder.h5',]# ParametersDATASETS = { 'original_youtube_videos': 'misc/downloaded_youtube_videos.zip', 'original_youtube_videos_info': 'misc/downloaded_youtube_videos_info.zip', 'original': 'original_sequences/youtube', 'DeepFakeDetection_original': 'original_sequences/actors', 'Deepfakes': 'manipulated_sequences/Deepfakes', 'DeepFakeDetection': 'manipulated_sequences/DeepFakeDetection', 'Face2Face': 'manipulated_sequences/Face2Face', 'FaceShifter': 'manipulated_sequences/FaceShifter', 'FaceSwap': 'manipulated_sequences/FaceSwap', 'NeuralTextures': 'manipulated_sequences/NeuralTextures' }ALL_DATASETS = ['original', 'DeepFakeDetection_original', 'Deepfakes', 'DeepFakeDetection', 'Face2Face', 'FaceShifter', 'FaceSwap', 'NeuralTextures']COMPRESSION = ['raw', 'c23', 'c40']TYPE = ['videos', 'masks', 'models']SERVERS = ['EU', 'EU2', 'CA']def parse_args(): parser = argparse.ArgumentParser( description='Downloads FaceForensics v2 public data release.', formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument('output_path', type=str, help='Output directory.') parser.add_argument('-d', '--dataset', type=str, default='all', help='Which dataset to download, either pristine or ' 'manipulated data or the downloaded youtube ' 'videos.', choices=list(DATASETS.keys()) + ['all'] ) parser.add_argument('-c', '--compression', type=str, default='raw', help='Which compression degree. All videos ' 'have been generated with h264 with a varying ' 'codec. Raw (c0) videos are lossless compressed.', choices=COMPRESSION ) parser.add_argument('-t', '--type', type=str, default='videos', help='Which file type, i.e. videos, masks, for our ' 'manipulation methods, models, for Deepfakes.', choices=TYPE ) parser.add_argument('-n', '--num_videos', type=int, default=None, help='Select a number of videos number to ' "download if you don't want to download the full" ' dataset.') parser.add_argument('--server', type=str, default='EU',
help='Server to download the data from. If you '
'encounter a slow download speed, consider '
'changing the server.',
choices=SERVERS
)
args = parser.parse_args()
# URLs
server = args.server
if server == 'EU':
server_url = 'http://canis.vc.in.tum.de:8100/'
elif server == 'EU2':
server_url = 'http://kaldir.vc.in.tum.de/faceforensics/'
elif server == 'CA':
server_url = 'http://falas.cmpt.sfu.ca:8100/'
else:
raise Exception('Wrong server name. Choices: {}'.format(str(SERVERS)))
args.tos_url = server_url + 'webpage/FaceForensics_TOS.pdf'
args.base_url = server_url + 'v3/'
args.deepfakes_model_url = server_url + 'v3/manipulated_sequences/' + \
'Deepfakes/models/'
return args
def download_files(filenames, base_url, output_path, report_progress=True): os.makedirs(output_path, exist_ok=True) if report_progress: filenames = tqdm(filenames) for filename in filenames: download_file(base_url + filename, join(output_path, filename))def reporthook(count, block_size, total_size): global start_time if count == 0: start_time = time.time() return duration = time.time() - start_time progress_size = int(count * block_size) speed = int(progress_size / (1024 * duration)) percent = int(count * block_size * 100 / total_size) sys.stdout.write("\rProgress: %d%%, %d MB, %d KB/s, %d seconds passed" % (percent, progress_size / (1024 * 1024), speed, duration)) sys.stdout.flush()def download_file(url, out_file, report_progress=False): out_dir = os.path.dirname(out_file) if not os.path.isfile(out_file): fh, out_file_tmp = tempfile.mkstemp(dir=out_dir) f = os.fdopen(fh, 'w') f.close() if report_progress: urllib.request.urlretrieve(url, out_file_tmp, reporthook=reporthook) else: urllib.request.urlretrieve(url, out_file_tmp) os.rename(out_file_tmp, out_file) else: tqdm.write('WARNING: skipping download of existing file ' + out_file)def main(args): # TOS print('By pressing any key to continue you confirm that you have agreed '\ 'to the FaceForensics terms of use as described at:') print(args.tos_url) print('***') print('Press any key to continue, or CTRL-C to exit.') _ = input('') # Extract arguments c_datasets = if args.dataset != 'all' else ALL_DATASETS c_type = args.type c_compression = args.compression num_videos = args.num_videos output_path = args.output_path os.makedirs(output_path, exist_ok=True) # Check for special dataset cases for dataset in c_datasets: dataset_path = DATASETS # Special cases if 'original_youtube_videos' in dataset: # Here we download the original youtube videos zip file print('Downloading original youtube videos.') if not 'info' in dataset_path: print('Please be patient, this may take a while (~40gb)') suffix = '' else: suffix = 'info' download_file(args.base_url + '/' + dataset_path, out_file=join(output_path, 'downloaded_videos{}.zip'.format( suffix)), report_progress=True) return # Else: regular datasets print('Downloading {} of dataset "{}"'.format( c_type, dataset_path )) # Get filelists and video lenghts list from server if 'DeepFakeDetection' in dataset_path or 'actors' in dataset_path: filepaths = json.loads(urllib.request.urlopen(args.base_url + '/' + DEEPFEAKES_DETECTION_URL).read().decode("utf-8")) if 'actors' in dataset_path: filelist = filepaths['actors'] else: filelist = filepaths['DeepFakesDetection'] elif 'original' in dataset_path: # Load filelist from server file_pairs = json.loads(urllib.request.urlopen(args.base_url + '/' + FILELIST_URL).read().decode("utf-8")) filelist = [] for pair in file_pairs: filelist += pair else: # Load filelist from server file_pairs = json.loads(urllib.request.urlopen(args.base_url + '/' + FILELIST_URL).read().decode("utf-8")) # Get filelist filelist = [] for pair in file_pairs: filelist.append('_'.join(pair)) if c_type != 'models': filelist.append('_'.join(pair[::-1])) # Maybe limit number of videos for download if num_videos is not None and num_videos > 0: print('Downloading the first {} videos'.format(num_videos)) filelist = filelist[:num_videos] # Server and local paths dataset_videos_url = args.base_url + '{}/{}/{}/'.format( dataset_path, c_compression, c_type) dataset_mask_url = args.base_url + '{}/{}/videos/'.format( dataset_path, 'masks', c_type) if c_type == 'videos': dataset_output_path = join(output_path, dataset_path, c_compression, c_type) print('Output path: {}'.format(dataset_output_path)) filelist = download_files(filelist, dataset_videos_url, dataset_output_path) elif c_type == 'masks': dataset_output_path = join(output_path, dataset_path, c_type, 'videos') print('Output path: {}'.format(dataset_output_path)) if 'original' in dataset: if args.dataset != 'all': print('Only videos available for original data. Aborting.') return else: print('Only videos available for original data. ' 'Skipping original.\n') continue if 'FaceShifter' in dataset: print('Masks not available for FaceShifter. Aborting.') return filelist = download_files(filelist, dataset_mask_url, dataset_output_path) # Else: models for deepfakes else: if dataset != 'Deepfakes' and c_type == 'models': print('Models only available for Deepfakes. Aborting') return dataset_output_path = join(output_path, dataset_path, c_type) print('Output path: {}'.format(dataset_output_path)) # Get Deepfakes models for folder in tqdm(filelist): folder_filelist = DEEPFAKES_MODEL_NAMES # Folder paths folder_base_url = args.deepfakes_model_url + folder + '/' folder_dataset_output_path = join(dataset_output_path, folder) download_files(folder_filelist, folder_base_url, folder_dataset_output_path, report_progress=False) # already doneif __name__ == "__main__": args = parse_args() main(args)
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